Cloudflare Pages Python API Docs | dltHub

Cloudflare Pages is a platform for building, deploying, and hosting Jamstack sites and static frontends with an API to manage projects, builds, and deployments. The REST API base URL is https://api.cloudflare.com/client/v4/accounts and all requests require a Bearer token for authentication.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Cloudflare Pages data in under 10 minutes.

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What data can I load from Cloudflare Pages?

Here are some of the endpoints you can load from Cloudflare Pages:

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How do I authenticate with the Cloudflare Pages API?

Use a Cloudflare API Token (recommended) or Global API Key in the Authorization header as "Authorization: Bearer ". Content-Type: application/json for JSON requests.

1. Get your credentials

1. Sign in to Cloudflare dashboard (https://dash.cloudflare.com). 2) Open My Profile → API Tokens. 3) To create a scoped token: click Create Token, choose permissions for Pages (Account.Pages:Read/Write as needed) and restrict to specific Account(s), then create and copy the token. 4) Alternatively, use the Global API Key from My Profile → API Keys (less recommended).

2. Add them to .dlt/secrets.toml

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[sources.cloudflare_pages_source]
token = "your_cloudflare_api_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Cloudflare Pages API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python cloudflare_pages_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline cloudflare_pages_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cloudflare_pages_data
The duckdb destination used duckdb:/cloudflare_pages.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline cloudflare_pages_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads pages_projects and pages_deployments from the Cloudflare Pages API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def cloudflare_pages_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cloudflare.com/client/v4/accounts",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "pages_projects", "endpoint": {"path": "accounts/{account_id}/pages/projects", "data_selector": "result"}},
            {"name": "pages_deployments", "endpoint": {"path": "accounts/{account_id}/pages/projects/{project_name}/deployments", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cloudflare_pages_pipeline",
        destination="duckdb",
        dataset_name="cloudflare_pages_data",
    )
    load_info = pipeline.run(cloudflare_pages_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("cloudflare_pages_pipeline").dataset()
sessions_df = data.pages_projects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cloudflare_pages_data.pages_projects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cloudflare_pages_pipeline").dataset()
data.pages_projects.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Cloudflare Pages data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

Ensure Authorization header is present: "Authorization: Bearer ". Tokens without Pages permissions or scoped to a different account will return 403 or errors. Use an API Token scoped to Account.Pages or the Global API Key.

Rate limiting

Cloudflare APIs may return 429 when rate limits are exceeded. Implement exponential backoff and inspect the Retry-After header.

Pagination

List endpoints return paginated results inside the top-level "result" key. Check API's pagination fields (e.g., result_info) to request subsequent pages with page and per_page query params.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack